A Name and Nations: The Right to Identity in a Divided World
Our story begins with a dark map of silence, millions of children whose births were never recorded and kept in dark. From this global view, the inequalities begin to speak louder.
A legal identity is not just a name on paper-its a child’s first step into recognition, protection, and access to rights like education, healthcare, and citizenship. But what happens when this fundamental right is not granted at birth?
This story explores global birth registration data (2020–2023), highlighting the stark inequalities across geography, gender, and economy. Using UNICEF data, we unpack how being born in the wrong place or as the wrong gender can make children invisible,statistically, legally, and socially. Through maps, charts, and contextual data, we unravel how economic development, gender norms, and crisis shocks collectively shape the identity landscape of under 5years age children worldwide.
Every Child Counts, But Not Every Child is Counted!!It reveals millions of children are never registered not because of who they are, but where they are born.
This map visualizes the global landscape of birth registration between 2020 and 2023.Each country is shaded based on the percentage of children under age 5 who are officially registered at birth. Hovering over a country reveals three key metrics: the percentage of registered children, the percentage of unregistered children, and the total national population.
The global view of birth registration reveals vast disparities between countries.Countries such as Nigeria, Yemen face institutional and conflict-related barriers that leave millions of children invisible. By contrast, nations like Canada, Japan achieve near-universal registration, thanks to strong systems and inclusive policies.Even in high-performing countries, the numbers are massive-in 2020, China’s 96.7% coverage still left 46.5 m approx children unregistered, nearly the population of Spain.
In the places where children are registered, are they registered equally? Not always. Being a girl can still mean being invisible.
In developed nations, gender equality begins at birth. Equal registration of boys and girls ensures both are counted, protected, and given access to rights like education and healthcare and many more.
In most of the high-income countries, both male and female registration rates are at 100% a clear sign of equality. Countries with perfect registration for both genders (e.g., Austria, Canada) contrast sharply with those where girls are still left behind.
This contrast reinforces a global reality: wealthier nations not only register more children but do so equitably across genders.This discrepancy begins at birth and compounds over time, contributing to long-term exclusion from services, rights, and social safety nets which essentially institutionalizing gender bias.
The right to identity isn’t always equal.In several regions, girls are left behind unregistered, unrecognized, and unprotected from birth.
Code
import pandas as pdfrom plotnine import*from IPython.display import HTMLimport plotly.io as pioimport plotly.express as pxdf = pd.read_csv("C:/Users/Trisha KN/OneDrive/Desktop/Project/Data/unicef_indicator_2.csv")df_gap = df[ (df['current_age'] =="Under 5 years old") & (df['time_period'] >=2020) & (df['time_period'] <=2023) & (df['sex'].isin(["Male", "Female"]))]pivoted = ( df_gap .groupby(['country', 'sex'], as_index=False, observed=False)['obs_value'] .mean() .pivot(index='country', columns='sex', values='obs_value') .dropna())pivoted['gender_gap'] = pivoted['Male'] - pivoted['Female']pivoted = pivoted.reset_index().sort_values('gender_gap', ascending=False)pivoted['country'] = pd.Categorical( pivoted['country'], categories=pivoted['country'], ordered=True)gender_gap_plot = ( ggplot(pivoted, aes(x='country', y='gender_gap', fill='gender_gap')) + geom_bar(stat='identity', show_legend=False) + geom_hline(yintercept=0, linetype="dashed", color="white") + labs( title='Gender Disparity in Birth Registration (2020–2023)', subtitle='↓ % of More boys are registered ↓ % of More girls are registered', x='Country', y='Avg. Gender Gap (Male - Female)' ) + scale_fill_gradient2(low="#00BFC4", mid="white", high="#F8766D", midpoint=0) + theme_minimal() + theme( figure_size=(10, 4), axis_text_x=element_text(rotation=90, size=7, color='white'), axis_text_y=element_text(color='white'), axis_title=element_text(color='white'), plot_title=element_text(size=13, weight='bold', color='white'), plot_subtitle=element_text(size=9, color='white'), panel_background=element_rect(fill="#1e1e1e"), plot_background=element_rect(fill="#1e1e1e"), panel_grid_major=element_line(color='#333333'), panel_grid_minor=element_blank() ))# gender_gap_plot#for better visualization i created a interactive one which is-fig = px.bar( pivoted, x='country', y='gender_gap', color='gender_gap', labels={'gender_gap': 'Gender Gap %'}, color_continuous_scale=[ "#00BFC4", "white", "#F8766D"], title='Gender Disparity in Birth Registration (2020–2023)', subtitle='↓ % of More boys are registered ↓ % of More girls are registered', hover_data={'country': True, 'gender_gap': ':.1f'})fig.update_traces( textposition='outside', marker_line_color='white', marker_line_width=0.5)fig.update_layout( xaxis_title="Country", yaxis_title="Avg. Gender Gap (Male - Female)", plot_bgcolor="#1e1e1e", paper_bgcolor="#1e1e1e", font_color="white", xaxis_tickangle=90, margin=dict(t=60, b=80), coloraxis_colorbar=dict( title="Gap", ticks="outside" ))HTML(pio.to_html(fig, full_html=False, include_plotlyjs='cdn'))
Countries like Swaziland, Malawi, and Nepal show significant levels of gender disparity in registration, with more boys being registered than girls. Even where systems exist, deep-rooted cultural norms may still prioritize sons over daughters.
According to UNICEF, girls in rural or patriarchal societies are often excluded from official systems, making them invisible in national statistics and vulnerable to being left behind in rights-based development.However, there are beacons of progress: countries like Tuvalu and Madagascar show gender-equal or female-favored registration rates, proving that equity is possible where inclusive values and effective policies align.
Code
from plotnine import*import pandas as pdgdp_recent = unicef_metadata[ (unicef_metadata['year'] >=2020) & (unicef_metadata['year'] <=2023) & (unicef_metadata['GDP per capita (constant 2015 US$)'].notna())]avg_gdp = ( gdp_recent.groupby('country', as_index=False)['GDP per capita (constant 2015 US$)'] .mean() .rename(columns={'GDP per capita (constant 2015 US$)': 'avg_gdp_per_capita'}))birth_df = df[(df['current_age'] =="Under 5 years old") &(df['time_period'] >=2020) &(df['time_period'] <=2023) &(df['sex'].isin(['Male', 'Female']))]avg_birth = ( birth_df.groupby('country', as_index=False)['obs_value'] .mean() .rename(columns={'obs_value': 'avg_birth_registration'}))merged_df = pd.merge(avg_gdp, avg_birth, on='country', how='inner')chart = ( ggplot(merged_df, aes(x='avg_gdp_per_capita', y='avg_birth_registration')) + geom_point(color='#99d9ea', size=3) + geom_smooth(method='lm', color='#99d9ea', se=False) + labs( title='GDP and birth registration co-relation (2020–2023)', x='Avg. GDP per capita', y='Avg. children under age 5 whose births are registered' ) + theme_minimal() + theme( figure_size=(6, 4), plot_title=element_text(weight='bold', style='italic', size=11, color='white'), axis_title=element_text(color='white'), axis_text=element_text(color='white'), panel_background=element_rect(fill='#222222'), plot_background=element_rect(fill='#222222'), panel_grid_major=element_line(color='#444444'), panel_grid_minor=element_blank(), ))chart
Birth registration is the first step toward legal identity. Without it, millions of children remain statistically invisible, lacking access to schooling, healthcare, and protection from exploitation.
Legal identity doesn’t exist in isolation, it relies on infrastructure, access, and governance, all deeply tied to a country’s economic capacity. This scatterplot reveals reality in stark terms.
Each point on this chart represents a country, comparing its GDP per capita with the share of under-5 children registered at birth between 2020 and 2023. The upward trendline shows that the birth registration rate increases as gdp tend to increase.
“Poverty doesn’t just steal food or shelter. It steals identity.”
The takeaway is sobering: millions of children remain invisible not by choice, but because their countries are too poor to count them. Until economic growth is matched with inclusive policies, the right to identity will remain out of reach for many.
According to UNICEF data, countries like Norway, Ireland, and Australia with GDPs over $30,000 achieve nearly 100% registration. In contrast, nations like Yemen, South Sudan, and Niger, with GDPs under $5,000, often register less than half their children.The upward trendline also confirmed that wealthier nations consistently register more children.
This line plot traces GDP per capita from 1960 to 2023 for India, China, Ireland, the UK, and the US-five nations reflecting distinct economic journeys and identity outcomes.Understanding GDP history shows how sustained economic development strengthens civil systems like birth registration, but growth alone isn’t enough. From China’s megacities to India’s farmlands, Ireland’s tech boom to the UK’s industrial shift, and the US’s dominance, prosperity has risen.
Yet the real question remains:
Does rising GDP ensure every child is seen and counted?
Code
unicef_metadata = pd.read_csv("C:/Users/Trisha KN/OneDrive/Desktop/Project/Data/unicef_metadata.csv")import plotly.express as pximport pandas as pddf = pd.read_csv("C:/Users/Trisha KN/OneDrive/Desktop/Project/Data/unicef_indicator_2.csv")unicef_metadata = pd.read_csv("C:/Users/Trisha KN/OneDrive/Desktop/Project/Data/unicef_metadata.csv")gdp_df = unicef_metadata[ unicef_metadata['country'].isin(['India', 'United States', 'Ireland', 'China', 'United Kingdom'])]fig = px.line( gdp_df, x='year', y='GDP per capita (constant 2015 US$)', color='country', markers=True, title='GDP per Capita Over Time', labels={'year': 'Year', 'GDP per capita (constant 2015 US$)': 'GDP per capita (2015 US$)', 'country': 'Country'})fig.update_layout( plot_bgcolor='black', paper_bgcolor='black', font_color='white')HTML(pio.to_html(fig, full_html=False, include_plotlyjs='cdn'))
United States
Long-term GDP stability has enabled near universal birth registration since the mid 20th century.Strong institutions,digital record keeping, and consistent investment ensure almost no child is left undocumented at birth.
Ireland
Once a struggling economy in the 1960s-80s, Ireland experienced dramatic growth post 1990s (“Celtic Tiger” era), with GDP per capita rising from under $10,000 in the 1980s to over $91,000 today.This economic leap enabled widespread digital transformation, including modernized civil registration services.Birth registration rates rose alongside economic transformation, with near total gender equality achieved through modernization.
China
While GDP per capita soared after 1978, birth registration lagged, especially for rural and migrant children due to the hukou system. China’s case highlights that economic growth alone doesn’t guarantee full registration without parallel reforms in social and legal inclusion.
India
India has shown steady, if slower, GDP growth since the 1991 economic reforms. While registration rates have improved significantly,gaps persist due to vast regional and rural-urban disparities. Still, initiatives like Aadhaar and the Civil Registration System are helping bridge those gaps, which plays critical role in converting economic growth into universal birth documentation.
United Kingdom
As one of the earliest countries to implement civil registration (in the 1800s), it showcases how long-term institutional investment, sustained by a stable GDP, results in strong documentation practices even during periods of economic stagnation. Its enduring civil registration system exemplifies how prosperity layered over time fosters resilience. Despite economic ups and downs, historical emphasis on documentation ensured that birth registration remained resilient and universal across generations.
But sustained progress isn’t guaranteed. What happens when economies stall, inflation rises, or new crises strike fragile systems?
Code
import pandas as pdimport matplotlib.pyplot as pltdf = pd.read_csv('C:/Users/Trisha KN/OneDrive/Desktop/Project/Data/unicef_indicator_2.csv')meta = pd.read_csv('C:/Users/Trisha KN/OneDrive/Desktop/Project/Data/unicef_metadata.csv')birth_total = df[(df["current_age"] =="Under 5 years old") & (df["sex"] =="Total") & (df["time_period"].between(2020, 2023))]birth_avg = birth_total.groupby("time_period", as_index=False)["obs_value"].mean()birth_avg.rename(columns={"time_period": "year", "obs_value": "Avg Registration (%)"}, inplace=True)gdp_filtered = meta[(meta["year"].between(2020, 2023)) & (meta["GDP per capita (constant 2015 US$)"].notna())]gdp_avg = gdp_filtered.groupby("year", as_index=False)["GDP per capita (constant 2015 US$)"].mean()fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), sharex=True)fig.patch.set_facecolor('#1e1e1e')ax1.set_facecolor('#1e1e1e')ax2.set_facecolor('#1e1e1e')for ax in [ax1, ax2]: ax.set_xticks([2020, 2021, 2022, 2023]) ax.set_xlabel("Year", color='white', fontsize=12) ax.spines['bottom'].set_color('white') ax.spines['top'].set_color('white') ax.spines['left'].set_color('white') ax.spines['right'].set_color('white') ax.tick_params(axis='x', colors='white') ax.tick_params(axis='y', colors='white')# Chart 1: Birth Registrationax1.plot(birth_avg["year"], birth_avg["Avg Registration (%)"], color='#00BFC4', linewidth=3, marker='o')ax1.set_title("Birth Registration (%)", color='white', fontsize=13, fontweight='bold')ax1.set_ylabel("children under age 5\nwhose births are registered (%)", color='white', fontsize=12)ax1.set_ylim(0, 100)ax1.set_yticks(range(0, 101, 20))# annotation ax1.annotate("Decline after 2021\n(possibly economic strain)", xy=(2022, birth_avg.loc[birth_avg['year'] ==2022, "Avg Registration (%)"].values[0]),xytext=(2020.5, 40), arrowprops=dict(arrowstyle="->", color='white'),fontsize=10, color='white',bbox=dict(boxstyle="round", fc="gray", alpha=0.5))# Chart 2: GDP per capitaax2.plot(gdp_avg["year"], gdp_avg["GDP per capita (constant 2015 US$)"], color='gray', linewidth=3, marker='o')ax2.set_title("GDP per Capita (US$)", color='white', fontsize=13, fontweight='bold')ax2.set_ylabel("GDP per capita\n(constant 2015 US$)", color='white', fontsize=12)ax2.set_ylim(0, gdp_avg["GDP per capita (constant 2015 US$)"].max() +5000)ax2.set_yticks(range(0, 30001, 5000))fig.suptitle("When Stability Isn't Enough: The Hidden Risk to Children's Identity (2020–2023)", color='white', fontsize=15, fontweight='bold')fig.tight_layout(rect=[0, 0, 1, 0.95])plt.show()
Earlier charts showed a hopeful link: stronger economies often had stronger birth registration systems. But prosperity isn’t permanent. What happens when economies stall or crises disrupt fragile services?
Chart 6 captures that shift. From 2020 to 2022, birth registration steadily improved, paralleling global recovery after the pandemic.However, in 2023, despite GDP per capita holding stable, registration rates dropped sharply breaking the upward trend.This disconnect reveals a deeper truth: it’s not just economic growth that matters-it’s the resilience of public services. Even without recession, factors like inflation, conflict, or budget reallocations can destabilize civil systems.Health centers and registration units, especially in rural or low resource settings, may be deprioritized,making children invisible by default.
The most affected are the most vulnerable: Children in rural areas, girls in patriarchal societies, and displaced or conflict affected communities those already at the margins.The 2023 drop is a warning:Stable GDP doesn’t guarantee protection if public systems crumble underneath.
In fragile contexts, building resilient identity services matters just as much as economic growth because without registration,millions remain invisible, unprotected, and forgotten.
Across these visualizations, a clear story emerges:Wealthier nations not only register more children, but they do so more equitably. But in lower income or crisis affected regions, millions of children especially girls remain invisible from birth.
While economic strength often enables robust identity systems,growth alone is not enough.Chart 6 drives this point home: even when GDP remains stable, birth registration can decline if public systems lack resilience.What matters most is not just prosperity,but the ability of institutions to protect identity rights in the face of adversity.be it inflation, conflict, or budget reallocations.
Children born into poverty, displacement, or patriarchal settings face compounded risks:they are the first to be left behind when systems falter. This is not just a data problem-it’s a justice problem.
Without a birth certificate, children are denied education, healthcare, legal protection, and often a future.And in 2023 alone, we saw how easily fragile progress can reverse. As countries strive toward Sustainable Development Goal 16 legal identity for all, including birth registration, the path forward is clear: We must invest in inclusive, gender equal, and crisis-resilient identity systems.Because every child counts but only if they are counted!!.
A name,A future,A right-every child deserves to be seen.Help build a world where no child is invisible.
